
# Import packages
import pandas as pd
import numpy as np
import os

# Plot Figures
import plotly.graph_objects as go
import chart_studio.plotly as py
import cufflinks as cf
import seaborn as sns
import matplotlib.pyplot as plt
cf.go_offline()

BATH_PATH = os.getcwd()
ETC_PATH = os.path.join(BATH_PATH, "..", "etc")
DATA_PATH = os.path.join(BATH_PATH, "..", "data")

Meta_table_file = os.path.join(ETC_PATH, "rtem_all_points_metadata.csv")


# Extract Necessary meta of building
def extract_necessary_meta(building_id):
    # Preprocess
    Meta_table_DF = pd.read_csv(Meta_table_file)
    Meta_table_DF.loc[:, "units"] = np.where(Meta_table_DF.loc[:, "units"].isna(), Meta_table_DF.loc[:, "tagged_units"], Meta_table_DF.loc[:, "units"])
    Meta_table_DF.loc[:, "name_point"] = np.where(Meta_table_DF.loc[:, "name_point"].isna(), Meta_table_DF.loc[:, "description"], Meta_table_DF.loc[:, "name_point"])
    # Select columns/building_id
    select_cols = ["id_point", "building_id", "name_point", "description", "units", "tagged_units", "equip_id", "equip_type_abbr", "equip_type_name", "tags"]
    Meta_table_DF = Meta_table_DF[Meta_table_DF["building_id"]==building_id][select_cols]
    return Meta_table_DF

building_id = 438
extract_necessary_meta(building_id)


# Save meta for building
def save_meta_building(building_id):
    DF = extract_necessary_meta(building_id)
    DF.reset_index(drop=True, inplace=True)
    try:
        DF.to_csv(os.path.join(DATA_PATH, F"Building_{building_id}", F"Meta_Building_{building_id}.csv"))
        return print(F"Meta_Building_{building_id}.csv saved!")
    except:
        return print(F"Meta_Building_{building_id}.csv saving failed!")

building_id = 438
save_meta_building(building_id)

Meta_Building_438.csv saved!


# Merge Building Data
def merge_building_data(building_id):
    Building_Data_path = os.path.join(DATA_PATH, F"Building_{building_id}")
    Building_Data_List = [device for device in os.listdir(Building_Data_path) if "rtem_API_data" in device]
    data_list = []
    for device in Building_Data_List:
        temp_data = pd.read_csv(os.path.join(DATA_PATH, F"Building_{building_id}", device))
        temp_data.set_index("timestamp", inplace=True)
        data_list.append(temp_data)
    res_DF = pd.concat(data_list, axis=1, join="outer")
    res_DF.index = pd.to_datetime(res_DF.index)
    for col in res_DF:
        res_DF.loc[:, col] = pd.to_numeric(res_DF.loc[:, col])
    return res_DF

building_id = 438
merged_data = merge_building_data(building_id)
display(merged_data)


# Select columns and standardize column names
def standardize_column_names(building_id):
    Building_Data_path = os.path.join(DATA_PATH, F"Building_{building_id}")
    # Point_Table
    Point_table_file = [point_table for point_table in os.listdir(Building_Data_path) if ("Point_Table" in point_table) & (point_table.endswith("v2.xlsx"))]
    Point_table = pd.read_excel(os.path.join(DATA_PATH, F"Building_{building_id}", Point_table_file[0]))
    Point_table.sort_values(by="Point_ID", inplace=True)
    select_cols = [str(col) for col in Point_table.loc[:, "Point_ID"]]
    # Process data
    data = merge_building_data(building_id).loc[:, select_cols]
    data.columns = list(Point_table.loc[:, "Standardized_Point_Name"])
    for idx in Point_table.index:
        if (Point_table.loc[idx, "Unit"] == "F") or (Point_table.loc[idx, "Unit"] == "f"):
            data.loc[:, Point_table.loc[idx, "Standardized_Point_Name"]] = (data.loc[:, Point_table.loc[idx, "Standardized_Point_Name"]].values - 32) * 5 / 9
        if (Point_table.loc[idx, "Unit"] == "GPM") or (Point_table.loc[idx, "Unit"] == "gpm"): # GPM to LPH
            data.loc[:, Point_table.loc[idx, "Standardized_Point_Name"]] = (data.loc[:, Point_table.loc[idx, "Standardized_Point_Name"]].values) * 227.1
    return data
    
building_id = 438
merged_data = standardize_column_names(building_id)
display(merged_data.head(5))

# OUTPUT_DATA_PATH = os.path.join(BATH_PATH, "..", "data", F"Building_{building_id}", "output_data")
# merged_data.to_csv(os.path.join(OUTPUT_DATA_PATH, F"merged_data_{building_id}.csv"))
# print(F"merged_data_{building_id} data saved!")


# Split Building Device 
def split_building_device(building_id):
    Building_Data_path = os.path.join(DATA_PATH, F"Building_{building_id}")
    Point_table_file = [point_table for point_table in os.listdir(Building_Data_path) if ("Point_Table" in point_table) & (point_table.endswith("v2.xlsx"))]
    Device_Table = pd.read_excel(os.path.join(DATA_PATH, F"Building_{building_id}", Point_table_file[0]), sheet_name="Device_class")
    Device_Table["Keywords"] = [keyword.replace(";", "|") for keyword in Device_Table["Keywords"]]
    data = standardize_column_names(building_id)
    # Classify Points by Device_Name
    Chiller_dictionary = {}
    filter_Table = pd.DataFrame.from_dict({"TCHD_cal":[0, 10], "RT_cal":[0, 50000], "CH_KW":[0, 50000], "KWRT_cal":[0, 3], "COP_cal":[0, 10]}, orient="index", columns=["Filter_bottom", "Filter_top"])
    for idx in Device_Table.index:
        chiller_name = Device_Table.loc[idx, "Device_Name"]
        chiller_data_cols = data.columns.str.contains(Device_Table.loc[idx, "Keywords"], regex=True)
        chiller_data = data.loc[:, chiller_data_cols].copy()
        if "CH" in chiller_name:
            for col in chiller_data.columns:
                try:
                    chiller_data.rename(columns={col: F'{col.split("-")[1]}'}, inplace=True)
                except:
                    pass
            chiller_data.drop(["CH_KWH"], axis=1, inplace=True)
            # Process chiller_data
            TCHD = chiller_data.loc[:, "TCHR"].values - chiller_data.loc[:, "TCHS"].values
            RT_cal = chiller_data.loc[:, "QCH"].values * TCHD / 3024 # USRT
            # RT_cal = chiller_data.loc[:, "QCH"].values * TCHD * 1000/3600 * 4.2 / 3.516
            KWRT_cal = chiller_data.loc[:, "CH_KW"] / RT_cal
            COP_cal = 3.516 / KWRT_cal
            chiller_data.loc[:, "TCHD_cal"] = TCHD
            chiller_data.loc[:, "RT_cal"] = RT_cal
            chiller_data.loc[:, "KWRT_cal"] = KWRT_cal
            chiller_data.loc[:, "COP_cal"] = COP_cal
        
            for col in chiller_data.columns:
                if col in filter_Table.index:
                    chiller_data.loc[:, col] = np.where(np.logical_or(chiller_data.loc[:, col]>filter_Table.loc[col, "Filter_top"], chiller_data.loc[:, col]<filter_Table.loc[col, "Filter_bottom"]), np.nan, chiller_data.loc[:, col])

        Chiller_dictionary[chiller_name] = chiller_data.dropna()
            
    return Chiller_dictionary

Chillers = split_building_device(building_id)
OUTPUT_DATA_PATH = os.path.join(BATH_PATH, "..", "data", F"Building_{building_id}", "output_data")

for chiller in Chillers.keys():
    temp_df = Chillers[chiller]
    display(temp_df.head(5))
    temp_df.to_csv(os.path.join(OUTPUT_DATA_PATH, F"{chiller}_data.csv"))
    print(F"{chiller} data saved!")

CH_22_3 data saved!

CH_22_4 data saved!

AHU data saved!


# Merge HVAC related data
def merge_HVAC_data(CH_mode, AHU_Device):
    OUTPUT_DATA_PATH = os.path.join(BATH_PATH, "..", "data", F"Building_{building_id}", "output_data")
    # HVAC_data_path = os.path.join()
    HVAC_data_list = [CH_mode, AHU_Device] if CH_mode!="Mix" else ["CH_22_3", "CH_22_4", AHU_Device]
    data_list = []
    if CH_mode == "Mix":
        for data in HVAC_data_list:
            temp = pd.read_csv(os.path.join(OUTPUT_DATA_PATH, F"{data}_data.csv"))
            temp.set_index("timestamp", inplace=True)
            if "CH" in data:
                temp.columns = [F"{data}-{col}" for col in temp.columns]
            if "AHU" in data:
                temp.columns = [F"{col.split('-')[1]}" for col in temp.columns]
            data_list.append(temp)
        Res_DF = pd.concat(data_list, axis=1, join="outer")
        Res_DF.loc[:, "Total_RT"] = Res_DF.filter(regex="-RT_cal").sum(axis=1)
        Res_DF.loc[:, "Total_CH_KW"] = Res_DF.filter(regex="-CH_KW").sum(axis=1)
        Res_DF.loc[:, "Total_CH_KWRT"] = Res_DF.loc[:, "Total_CH_KW"] / Res_DF.loc[:, "Total_RT"]
        Res_DF.loc[:, "Twb_cal"] = Res_DF.filter(regex="TCWR_").mean(axis=1)
        Res_DF.loc[:, "TCHD_cal"] = Res_DF.filter(regex="-TCHD_cal").mean(axis=1)
        Res_DF.loc[:, "QCH"] = Res_DF.filter(regex="-QCH").mean(axis=1) / 1000
        Res_DF.loc[:, "Total_RT"] = np.where(Res_DF.loc[:, "Total_RT"]<=0, np.nan, Res_DF.loc[:, "Total_RT"])
        Res_DF.loc[:, "AHU_CCVC"] = np.where(Res_DF.loc[:, "AHU_CCVC"]<=0, np.nan, Res_DF.loc[:, "AHU_CCVC"])
    else:
        for data in HVAC_data_list:
            temp = pd.read_csv(os.path.join(OUTPUT_DATA_PATH, F"{data}_data.csv"))
            temp.set_index("timestamp", inplace=True)
            if "AHU" in data:
                temp.columns = [F"{col.split('-')[1]}" for col in temp.columns]
            data_list.append(temp)
        Res_DF = pd.concat(data_list, axis=1, join="outer")
        Res_DF.loc[:, "AHU_OCC"] = np.where(Res_DF.loc[:, "AHU_OCC"]==0, np.nan, 1)
        Res_DF.loc[:, "Twb_cal"] = Res_DF.filter(regex="TCWR_").mean(axis=1)
        Res_DF.loc[:, "Total_RT"] = Res_DF.filter(regex="RT_cal").sum(axis=1)
        Res_DF.loc[:, "Total_CH_KWRT"] = Res_DF.loc[:, "CH_KW"] / Res_DF.loc[:, "Total_RT"]
        Res_DF.loc[:, "QCH"] = Res_DF.loc[:, "QCH"] / 1000
        Res_DF.loc[:, "Total_RT"] = np.where(Res_DF.loc[:, "Total_RT"]<=0, np.nan, Res_DF.loc[:, "Total_RT"])
        Res_DF.loc[:, "AHU_CCVC"] = np.where(Res_DF.loc[:, "AHU_CCVC"]<=0, np.nan, Res_DF.loc[:, "AHU_CCVC"])
    return Res_DF.dropna()
CH_mode = "CH_22_3"
AHU_Device = "AHU_15"
test = merge_HVAC_data(CH_mode, AHU_Device)
display(test.head())


# Compare
X_targ = "AHU_TAS"
Y_targ = "TASD"
Hue = "TCHD_class"

test_dict = {"CH_22_3":{}, "CH_22_4": {}, "Mix": {}}
for ch in ["CH_22_3", "CH_22_4", "Mix"]:
    for ahu in ["AHU_5", "AHU_7", "AHU_15"]:
        temp = merge_HVAC_data(ch, ahu)
        temp.loc[:, "TASD"] = temp["AHU_TAS"] - temp["AHU_TAS_SP"]
        # temp.loc[:, "TASD"] = np.where(temp.loc[:, "TASD"]<0, np.nan, temp.loc[:, "TASD"])
        temp.loc[:, "TCHD_class"] = np.where(np.logical_and(temp.loc[:, "TCHD_cal"]>=7, temp.loc[:, "TCHD_cal"]<9), 8, 10)
        temp.loc[:, "TCHD_class"] = np.where(np.logical_and(temp.loc[:, "TCHD_cal"]>=5, temp.loc[:, "TCHD_cal"]<7), 6, temp.loc[:, "TCHD_class"])
        temp.loc[:, "TCHD_class"] = np.where(np.logical_and(temp.loc[:, "TCHD_cal"]>=3, temp.loc[:, "TCHD_cal"]<5), 4, temp.loc[:, "TCHD_class"])
        temp.loc[:, "TCHD_class"] = np.where(np.logical_and(temp.loc[:, "TCHD_cal"]>=1, temp.loc[:, "TCHD_cal"]<3), 2, temp.loc[:, "TCHD_class"])
        temp.loc[:, "TCHD_class"] = np.where(np.logical_and(temp.loc[:, "TCHD_cal"]>=-1, temp.loc[:, "TCHD_cal"]<1), 0, temp.loc[:, "TCHD_class"])
        test_dict[ch][ahu] = temp

fig, ax = plt.subplots(3, 3, figsize=(18, 18))
for ch, idx in zip(["CH_22_3", "CH_22_4", "Mix"], [0, 1, 2]):
    for ahu, idy in zip(["AHU_5", "AHU_7", "AHU_15"], [0, 1, 2]):
        sns.scatterplot(ax=ax[idx, idy], data=test_dict[ch][ahu], x=X_targ, y=Y_targ, hue=Hue, alpha=0.25, palette="hot_r")
        ax[idx, idy].set_title(F"CH: {ch}, AHU: {ahu}")
        ax[idx, idy].axhline(y=0, xmin=0, xmax=1, color="grey")


# Compare "AHU_CCVC"
X_targ = "AHU_TAS"
Y_targ = "TASD"
Hue = "AHU_CCVC"

test_dict = {"CH_22_3":{}, "CH_22_4": {}, "Mix": {}}
for ch in ["CH_22_3", "CH_22_4", "Mix"]:
    for ahu in ["AHU_5", "AHU_7", "AHU_15"]:
        temp = merge_HVAC_data(ch, ahu)
        temp.loc[:, "TASD"] = temp["AHU_TAS"] - temp["AHU_TAS_SP"]
        # temp.loc[:, "TASD"] = np.where(temp.loc[:, "TASD"]<0, np.nan, temp.loc[:, "TASD"])
        temp.loc[:, "TCHD_class"] = np.where(np.logical_and(temp.loc[:, "TCHD_cal"]>=7, temp.loc[:, "TCHD_cal"]<9), 8, 10)
        temp.loc[:, "TCHD_class"] = np.where(np.logical_and(temp.loc[:, "TCHD_cal"]>=5, temp.loc[:, "TCHD_cal"]<7), 6, temp.loc[:, "TCHD_class"])
        temp.loc[:, "TCHD_class"] = np.where(np.logical_and(temp.loc[:, "TCHD_cal"]>=3, temp.loc[:, "TCHD_cal"]<5), 4, temp.loc[:, "TCHD_class"])
        temp.loc[:, "TCHD_class"] = np.where(np.logical_and(temp.loc[:, "TCHD_cal"]>=1, temp.loc[:, "TCHD_cal"]<3), 2, temp.loc[:, "TCHD_class"])
        temp.loc[:, "TCHD_class"] = np.where(np.logical_and(temp.loc[:, "TCHD_cal"]>=-1, temp.loc[:, "TCHD_cal"]<1), 0, temp.loc[:, "TCHD_class"])
        test_dict[ch][ahu] = temp

fig, ax = plt.subplots(3, 3, figsize=(16, 16))
for ch, idx in zip(["CH_22_3", "CH_22_4", "Mix"], [0, 1, 2]):
    for ahu, idy in zip(["AHU_5", "AHU_7", "AHU_15"], [0, 1, 2]):
        sns.scatterplot(ax=ax[idx, idy], data=test_dict[ch][ahu], x=X_targ, y=Y_targ, hue=Hue, alpha=0.25, palette="hot_r")
        ax[idx, idy].set_title(F"CH: {ch}, AHU: {ahu}")
        ax[idx, idy].axhline(y=0, xmin=0, xmax=1, color="grey")


# Compare
X_targ = "AHU_TAS"
Y_targ = "TASD"
Hue = "Total_RT"

test_dict = {"CH_22_3":{}, "CH_22_4": {}, "Mix": {}}
for ch in ["CH_22_3", "CH_22_4", "Mix"]:
    for ahu in ["AHU_5", "AHU_7", "AHU_15"]:
        temp = merge_HVAC_data(ch, ahu)
        temp.loc[:, "TASD"] = temp["AHU_TAS"] - temp["AHU_TAS_SP"]
        # temp.loc[:, "TASD"] = np.where(temp.loc[:, "TASD"]<0, np.nan, temp.loc[:, "TASD"])
        temp.loc[:, "TCHD_class"] = np.where(np.logical_and(temp.loc[:, "TCHD_cal"]>=7, temp.loc[:, "TCHD_cal"]<9), 8, 10)
        temp.loc[:, "TCHD_class"] = np.where(np.logical_and(temp.loc[:, "TCHD_cal"]>=5, temp.loc[:, "TCHD_cal"]<7), 6, temp.loc[:, "TCHD_class"])
        temp.loc[:, "TCHD_class"] = np.where(np.logical_and(temp.loc[:, "TCHD_cal"]>=3, temp.loc[:, "TCHD_cal"]<5), 4, temp.loc[:, "TCHD_class"])
        temp.loc[:, "TCHD_class"] = np.where(np.logical_and(temp.loc[:, "TCHD_cal"]>=1, temp.loc[:, "TCHD_cal"]<3), 2, temp.loc[:, "TCHD_class"])
        temp.loc[:, "TCHD_class"] = np.where(np.logical_and(temp.loc[:, "TCHD_cal"]>=-1, temp.loc[:, "TCHD_cal"]<1), 0, temp.loc[:, "TCHD_class"])
        test_dict[ch][ahu] = temp

fig, ax = plt.subplots(3, 3, figsize=(16, 16))
for ch, idx in zip(["CH_22_3", "CH_22_4", "Mix"], [0, 1, 2]):
    for ahu, idy in zip(["AHU_5", "AHU_7", "AHU_15"], [0, 1, 2]):
        sns.scatterplot(ax=ax[idx, idy], data=test_dict[ch][ahu], x=X_targ, y=Y_targ, hue=Hue, alpha=0.5, palette="hot_r")
        ax[idx, idy].set_title(F"CH: {ch}, AHU: {ahu}")
        ax[idx, idy].axhline(y=0, xmin=0, xmax=1, color="grey")


# Compare
X_targ = "AHU_TAS"
Y_targ = "TASD"
Hue = "QCH"

test_dict = {"CH_22_3":{}, "CH_22_4": {}, "Mix": {}}
for ch in ["CH_22_3", "CH_22_4", "Mix"]:
    for ahu in ["AHU_5", "AHU_7", "AHU_15"]:
        temp = merge_HVAC_data(ch, ahu)
        temp.loc[:, "TASD"] = temp["AHU_TAS"] - temp["AHU_TAS_SP"]
        # temp.loc[:, "TASD"] = np.where(temp.loc[:, "TASD"]<0, np.nan, temp.loc[:, "TASD"])
        temp.loc[:, "TCHD_class"] = np.where(np.logical_and(temp.loc[:, "TCHD_cal"]>=7, temp.loc[:, "TCHD_cal"]<9), 8, 10)
        temp.loc[:, "TCHD_class"] = np.where(np.logical_and(temp.loc[:, "TCHD_cal"]>=5, temp.loc[:, "TCHD_cal"]<7), 6, temp.loc[:, "TCHD_class"])
        temp.loc[:, "TCHD_class"] = np.where(np.logical_and(temp.loc[:, "TCHD_cal"]>=3, temp.loc[:, "TCHD_cal"]<5), 4, temp.loc[:, "TCHD_class"])
        temp.loc[:, "TCHD_class"] = np.where(np.logical_and(temp.loc[:, "TCHD_cal"]>=1, temp.loc[:, "TCHD_cal"]<3), 2, temp.loc[:, "TCHD_class"])
        temp.loc[:, "TCHD_class"] = np.where(np.logical_and(temp.loc[:, "TCHD_cal"]>=-1, temp.loc[:, "TCHD_cal"]<1), 0, temp.loc[:, "TCHD_class"])
        test_dict[ch][ahu] = temp

fig, ax = plt.subplots(3, 3, figsize=(16, 16))
for ch, idx in zip(["CH_22_3", "CH_22_4", "Mix"], [0, 1, 2]):
    for ahu, idy in zip(["AHU_5", "AHU_7", "AHU_15"], [0, 1, 2]):
        df = test_dict[ch][ahu].dropna()
        sns.scatterplot(ax=ax[idx, idy], data=df, x=X_targ, y=Y_targ, hue=Hue, alpha=0.25, palette="hot_r")
        ax[idx, idy].set_title(F"CH: {ch}, AHU: {ahu}")
        ax[idx, idy].axhline(y=0, xmin=0, xmax=1, color="grey")


# Compare
X_targ = "AHU_TAS"
Y_targ = "TASD"
Hue = "Total_CH_KWRT"

test_dict = {"CH_22_3":{}, "CH_22_4": {}, "Mix": {}}
for ch in ["CH_22_3", "CH_22_4", "Mix"]:
    for ahu in ["AHU_5", "AHU_7", "AHU_15"]:
        temp = merge_HVAC_data(ch, ahu)
        temp.loc[:, "TASD"] = temp["AHU_TAS"] - temp["AHU_TAS_SP"]
        # temp.loc[:, "TASD"] = np.where(temp.loc[:, "TASD"]<0, np.nan, temp.loc[:, "TASD"])
        temp.loc[:, "TCHD_class"] = np.where(np.logical_and(temp.loc[:, "TCHD_cal"]>=7, temp.loc[:, "TCHD_cal"]<9), 8, 10)
        temp.loc[:, "TCHD_class"] = np.where(np.logical_and(temp.loc[:, "TCHD_cal"]>=5, temp.loc[:, "TCHD_cal"]<7), 6, temp.loc[:, "TCHD_class"])
        temp.loc[:, "TCHD_class"] = np.where(np.logical_and(temp.loc[:, "TCHD_cal"]>=3, temp.loc[:, "TCHD_cal"]<5), 4, temp.loc[:, "TCHD_class"])
        temp.loc[:, "TCHD_class"] = np.where(np.logical_and(temp.loc[:, "TCHD_cal"]>=1, temp.loc[:, "TCHD_cal"]<3), 2, temp.loc[:, "TCHD_class"])
        temp.loc[:, "TCHD_class"] = np.where(np.logical_and(temp.loc[:, "TCHD_cal"]>=-1, temp.loc[:, "TCHD_cal"]<1), 0, temp.loc[:, "TCHD_class"])
        test_dict[ch][ahu] = temp

fig, ax = plt.subplots(3, 3, figsize=(16, 16))
for ch, idx in zip(["CH_22_3", "CH_22_4", "Mix"], [0, 1, 2]):
    for ahu, idy in zip(["AHU_5", "AHU_7", "AHU_15"], [0, 1, 2]):
        sns.scatterplot(ax=ax[idx, idy], data=test_dict[ch][ahu], x=X_targ, y=Y_targ, hue=Hue, alpha=0.25, palette="hot_r")
        ax[idx, idy].set_title(F"CH: {ch}, AHU: {ahu}")
        ax[idx, idy].axhline(y=0, xmin=0, xmax=1, color="grey")


# RT vs KWRT 1
Load_Data_List = []
for chiller in Chillers.keys():
    if "CH" in chiller:
        temp_df = Chillers[chiller].copy()
        temp_df.loc[:, "Chiller_Name"] = chiller
        temp_df.loc[:, "QCH"] = temp_df.loc[:, "QCH"] / 1000
        temp_df.loc[:, "RT_cal"] = np.where(temp_df.loc[:, "RT_cal"]<=0, np.nan, temp_df.loc[:, "RT_cal"])
        temp.dropna(inplace=True)
        print(F"{chiller} data read: ")
        display(temp_df.head(5))
        Load_Data_List.append(temp_df)

        X_targ = "RT_cal"
        Y_targ = "COP_cal"

        # Plot
        fig, ax = plt.subplots(1, 3, figsize=(27, 8))
        for Hue, idx in zip(["TCHD_cal", "TCHS", "QCH"], [0, 1, 2]):
            sns.scatterplot(ax=ax[idx], data=temp_df, x=X_targ, y=Y_targ, hue=Hue, alpha=0.4, palette="hot_r")
            ax[idx].set_title(F"CH: {chiller} (displayed by {Hue})")

CH_22_3 data read:

CH_22_4 data read:


# RT vs KWRT 2
Load_Data_List = []
for chiller in Chillers.keys():
    if "CH" in chiller:
        temp_df = Chillers[chiller].copy()
        temp_df.loc[:, "Chiller_Name"] = chiller
        temp_df.loc[:, "QCH"] = temp_df.loc[:, "QCH"] / 1000
        temp_df.loc[:, "RT_cal"] = np.where(temp_df.loc[:, "RT_cal"]<=0, np.nan, temp_df.loc[:, "RT_cal"])
        temp.dropna(inplace=True)
        print(F"{chiller} data read: ")
        display(temp_df.head(5))
        Load_Data_List.append(temp_df)

        X_targ = "RT_cal"
        Y_targ = "KWRT_cal"

        # Plot
        fig, ax = plt.subplots(1, 3, figsize=(27, 8))
        for Hue, idx in zip(["TCHD_cal", "TCHS", "QCH"], [0, 1, 2]):
            sns.scatterplot(ax=ax[idx], data=temp_df, x=X_targ, y=Y_targ, hue=Hue, alpha=0.4, palette="hot_r")
            ax[idx].set_title(F"CH: {chiller} (displayed by {Hue})")

CH_22_3 data read:

CH_22_4 data read:


# RT vs KWRT 3
Load_Data_List = []
for chiller in Chillers.keys():
    if "CH" in chiller:
        temp_df = Chillers[chiller].copy()
        temp_df.loc[:, "Chiller_Name"] = chiller
        temp_df.loc[:, "QCH"] = temp_df.loc[:, "QCH"] / 1000
        temp_df.loc[:, "RT_cal"] = np.where(temp_df.loc[:, "RT_cal"]<=0, np.nan, temp_df.loc[:, "RT_cal"])
        temp.dropna(inplace=True)
        print(F"{chiller} data read: ")
        display(temp_df.head(5))
        Load_Data_List.append(temp_df)

        X_targ = "RT_cal"
        Y_targ = "KWRT_cal"

        # Plot
        Fig = go.Figure()
        Fig.add_trace(go.Scatter(name="RT-KWRT", x=temp_df[X_targ], y=temp_df[Y_targ], mode="markers"))
        Fig.update_layout(
            title = dict(text=F"Cooling Load vs KW/RT (Chiller: {chiller})", x=0.04, y=0.83),
            xaxis = dict(
                title=dict(text="Cooling Load (RT)", font=dict(size=12), standoff=0),
                showline=True, linewidth=1.2, linecolor="black",
                showticklabels=True, showgrid=True, gridcolor="rgba(230, 230, 230, 1)",
                rangeslider=dict(visible=False)),
            yaxis = dict(
                title=dict(text="Energy Performance (KW/RT)", font=dict(size=12), standoff=0),
                dtick=0.1,
                showline=True, linewidth=1.2, linecolor="black",
                zeroline=True, zerolinewidth=1.2, zerolinecolor="rgba(230, 230, 230, 1)",
                showgrid=True, gridcolor="rgba(230, 230, 230, 1)"),
            legend = dict(x=1.02, y=0.5, orientation="v", bordercolor="black", borderwidth=0.5, font=dict(size=8)),
            plot_bgcolor="white", width=700, height=400)
        Fig.show()

CH_22_3 data read:

CH_22_4 data read:


# Plot Timeseries & Analysis Table
Total_Load_cal = pd.concat(Load_Data_List, axis=1, join="outer")
Total_Load_cal.loc[:, "RT_total"] = Total_Load_cal["RT_cal"].sum(axis=1)
Load_Data_DF = pd.concat(Load_Data_List, axis=0)
Chiller_List = list(Load_Data_DF.loc[:, "Chiller_Name"].unique())
Fig = go.Figure()
for chiller in Chiller_List:
    Fig.add_trace(go.Scatter(name=chiller, x=Load_Data_DF[Load_Data_DF["Chiller_Name"]==chiller].index, y=Load_Data_DF[Load_Data_DF["Chiller_Name"]==chiller]["RT_cal"]))
# Fig.add_trace(go.Scatter(name="RT_total", x=Total_Load_cal.index, y=Total_Load_cal["RT_total"], marker=dict(color="rgba(102, 204, 153, 0.8)")))
Fig.update_layout(
    title = dict(text=F"Cooling Load Timeseries", x=0.04, y=0.83),
    xaxis = dict(
        showline=True, linewidth=1.2, linecolor="black",
        showticklabels=True, showgrid=True, gridcolor="rgba(230, 230, 230, 1)",
        rangeslider=dict(visible=False)),
    yaxis = dict(
        title=dict(text="Cooling Load (RT)", font=dict(size=12), standoff=0),
        showline=True, linewidth=1.2, linecolor="black",
        zeroline=True, zerolinewidth=1.2, zerolinecolor="rgba(230, 230, 230, 1)",
        showgrid=True, gridcolor="rgba(230, 230, 230, 1)"),
    legend = dict(x=1.02, y=0.5, orientation="v", bordercolor="black", borderwidth=0.5, font=dict(size=8)),
    plot_bgcolor="white", width=1000, height=400)
Fig.show()


# TCH Analysis
Load_Data_DF.reset_index(drop=False, inplace=True)
TCH_analysis = Load_Data_DF.pivot_table(index="timestamp", columns="Chiller_Name", values=["TCHS", "TCHR"]).describe().round(2)
print("Chilled Water Analysis")
display(TCH_analysis)

DF = Load_Data_DF.pivot_table(index="timestamp", columns="Chiller_Name", values=["TCHS", "TCHR"])
x_targ = DF.index
fig = go.Figure()
for Chiller_name, color_thick, color_thin in zip(Chiller_List, ["#2CBDFE", "#00cc99"], ["#80d6fe", "#b3ffec"]):
    try:
        y_targ1 = DF["TCHS"][Chiller_name]
        y_targ2 = DF["TCHR"][Chiller_name]
        fig.add_trace(go.Scatter(name=F"{Chiller_name}-TCHS", x=x_targ, y=y_targ1, mode="lines", line=dict(color=color_thick), showlegend=True))
        fig.add_trace(go.Scatter(name=F"{Chiller_name}-TCHR", x=x_targ, y=y_targ2, mode="lines", line=dict(color=color_thin), showlegend=True))
    except:
        print(F"{Chiller_name} Data is not sufficient to plot...\n")

fig.update_layout(
    title = dict(text=F"TCH Timeseries", x=0.04, y=0.83),
    xaxis = dict(
        showline=True, linewidth=1.2, linecolor="black",
        showticklabels=True, showgrid=True, gridcolor="rgba(230, 230, 230, 1)",
        rangeslider=dict(visible=False)),
    yaxis = dict(
        title=dict(text="Temp. (<sup>o</sup>C)", font=dict(size=12), standoff=0),
        showline=True, linewidth=1.2, linecolor="black",
        zeroline=True, zerolinewidth=1.2, zerolinecolor="rgba(230, 230, 230, 1)",
        showgrid=True, gridcolor="rgba(230, 230, 230, 1)"),
    legend = dict(x=1.02, y=0.5, orientation="v", bordercolor="black", borderwidth=0.5, font=dict(size=8)),
    plot_bgcolor="white", width=1000, height=400)
fig.show()

Chilled Water Analysis


# QCH Analysis
Load_Data_DF.reset_index(drop=False, inplace=True)
QCH_analysis = Load_Data_DF.pivot_table(index="timestamp", columns="Chiller_Name", values=["QCH"]).describe().round(2)
print("QCH Analysis")
display(QCH_analysis)

DF = Load_Data_DF.pivot_table(index="timestamp", columns="Chiller_Name", values=["QCH"])
x_targ = DF.index
fig = go.Figure()
for Chiller_name, color_thick, color_thin in zip(Chiller_List, ["#2CBDFE", "#00cc99"], ["#80d6fe", "#b3ffec"]):
    try:
        y_targ1 = DF["QCH"][Chiller_name]
        fig.add_trace(go.Scatter(name=F"{Chiller_name}-QCH", x=x_targ, y=y_targ1, mode="lines", line=dict(color=color_thick), showlegend=True))
    except:
        print(F"{Chiller_name} Data is not sufficient to plot...\n")

fig.update_layout(
    title = dict(text=F"QCH Timeseries", x=0.04, y=0.83),
    xaxis = dict(
        showline=True, linewidth=1.2, linecolor="black",
        showticklabels=True, showgrid=True, gridcolor="rgba(230, 230, 230, 1)",
        rangeslider=dict(visible=False)),
    yaxis = dict(
        title=dict(text="QCH (CMH)", font=dict(size=12), standoff=0),
        showline=True, linewidth=1.2, linecolor="black",
        zeroline=True, zerolinewidth=1.2, zerolinecolor="rgba(230, 230, 230, 1)",
        showgrid=True, gridcolor="rgba(230, 230, 230, 1)"),
    legend = dict(x=1.02, y=0.5, orientation="v", bordercolor="black", borderwidth=0.5, font=dict(size=8)),
    plot_bgcolor="white", width=1000, height=400)
fig.show()

QCH Analysis


# TCW Analysis
Load_Data_DF.loc[:, "TCWR"] = Load_Data_DF.loc[:, ["TCWR_1", "TCWR_2", "TCWR_3", "TCWR_4", "TCWR_5"]].mean(axis=1)
TCW_analysis = Load_Data_DF.pivot_table(index="timestamp", columns="Chiller_Name", values=["TCWS", "TCWR"]).describe().round(2)
print("Cooling Water Analysis")
display(TCW_analysis)

DF = Load_Data_DF.pivot_table(index="timestamp", columns="Chiller_Name", values=["TCWS", "TCWR"])
x_targ = DF.index
fig = go.Figure()
for Chiller_name, color_thick, color_thin in zip(Chiller_List, ["#2CBDFE", "#00cc99"], ["#80d6fe", "#b3ffec"]):
    try:
        y_targ1 = DF["TCWS"][Chiller_name]
        y_targ2 = DF["TCWR"][Chiller_name]
        fig.add_trace(go.Scatter(name=F"{Chiller_name}-TCWS", x=x_targ, y=y_targ1, mode="lines", line=dict(color=color_thick), showlegend=True))
        fig.add_trace(go.Scatter(name=F"{Chiller_name}-TCWR", x=x_targ, y=y_targ2, mode="lines", line=dict(color=color_thin), showlegend=True))
    except:
        print(F"{Chiller_name} Data is not sufficient to plot...\n")

fig.update_layout(
    title = dict(text=F"TCW Timeseries", x=0.04, y=0.83),
    xaxis = dict(
        showline=True, linewidth=1.2, linecolor="black",
        showticklabels=True, showgrid=True, gridcolor="rgba(230, 230, 230, 1)",
        rangeslider=dict(visible=False)),
    yaxis = dict(
        title=dict(text="Temp. (<sup>o</sup>C)", font=dict(size=12), standoff=0),
        showline=True, linewidth=1.2, linecolor="black",
        zeroline=True, zerolinewidth=1.2, zerolinecolor="rgba(230, 230, 230, 1)",
        showgrid=True, gridcolor="rgba(230, 230, 230, 1)"),
    legend = dict(x=1.02, y=0.5, orientation="v", bordercolor="black", borderwidth=0.5, font=dict(size=8)),
    plot_bgcolor="white", width=1000, height=400)
fig.show()

Cooling Water Analysis


# RT Analysis
RT_analysis = Load_Data_DF.pivot_table(index="timestamp", columns="Chiller_Name", values=["RT_cal"]).describe().round(2)
print("Cooling Load Analysis")
display(RT_analysis)

DF = Load_Data_DF.pivot_table(index="timestamp", columns="Chiller_Name", values=["RT_cal"])
x_targ = DF.index
fig = go.Figure()
for Chiller_name, color_thick, color_thin in zip(Chiller_List, ["#2CBDFE", "#00cc99"], ["#80d6fe", "#b3ffec"]):
    try:
        y_targ1 = DF["RT_cal"][Chiller_name]
        fig.add_trace(go.Scatter(name=F"{Chiller_name}-RT", x=x_targ, y=y_targ1, mode="lines", line=dict(color=color_thick), showlegend=True))
    except:
        print(F"{Chiller_name} Data is not sufficient to plot...\n")

fig.update_layout(
    title = dict(text=F"Cooling Load Timeseries", x=0.04, y=0.83),
    xaxis = dict(
        showline=True, linewidth=1.2, linecolor="black",
        showticklabels=True, showgrid=True, gridcolor="rgba(230, 230, 230, 1)",
        rangeslider=dict(visible=False)),
    yaxis = dict(
        title=dict(text="Cooling Load (USRT)", font=dict(size=12), standoff=0),
        showline=True, linewidth=1.2, linecolor="black",
        zeroline=True, zerolinewidth=1.2, zerolinecolor="rgba(230, 230, 230, 1)",
        showgrid=True, gridcolor="rgba(230, 230, 230, 1)"),
    legend = dict(x=1.02, y=0.5, orientation="v", bordercolor="black", borderwidth=0.5, font=dict(size=8)),
    plot_bgcolor="white", width=1000, height=400)
fig.show()

Cooling Load Analysis


# KWRT Analysis
COP_analysis = Load_Data_DF.pivot_table(index="timestamp", columns="Chiller_Name", values=["COP_cal"]).describe().round(2)
print("Chiller Performance Analysis")
display(COP_analysis)

DF = Load_Data_DF.pivot_table(index="timestamp", columns="Chiller_Name", values=["COP_cal"])
x_targ = DF.index
fig = go.Figure()
for Chiller_name, color_thick, color_thin in zip(Chiller_List, ["#2CBDFE", "#00cc99"], ["#80d6fe", "#b3ffec"]):
    try:
        y_targ1 = DF["COP_cal"][Chiller_name]
        fig.add_trace(go.Scatter(name=F"{Chiller_name}-COP", x=x_targ, y=y_targ1, mode="lines", line=dict(color=color_thick), showlegend=True))
    except:
        print(F"{Chiller_name} Data is not sufficient to plot...\n")

fig.update_layout(
    title = dict(text=F"Chiller Performance Timeseries", x=0.04, y=0.83),
    xaxis = dict(
        showline=True, linewidth=1.2, linecolor="black",
        showticklabels=True, showgrid=True, gridcolor="rgba(230, 230, 230, 1)",
        rangeslider=dict(visible=False)),
    yaxis = dict(
        title=dict(text="Chiller Performance (COP)", font=dict(size=12), standoff=0),
        showline=True, linewidth=1.2, linecolor="black",
        zeroline=True, zerolinewidth=1.2, zerolinecolor="rgba(230, 230, 230, 1)",
        showgrid=True, gridcolor="rgba(230, 230, 230, 1)"),
    legend = dict(x=1.02, y=0.5, orientation="v", bordercolor="black", borderwidth=0.5, font=dict(size=8)),
    plot_bgcolor="white", width=1000, height=400)
fig.show()

Chiller Performance Analysis


# KWRT Analysis
KWRT_analysis = Load_Data_DF.pivot_table(index="timestamp", columns="Chiller_Name", values=["KWRT_cal"]).describe().round(2)
print("Chiller Performance Analysis")
display(KWRT_analysis)

DF = Load_Data_DF.pivot_table(index="timestamp", columns="Chiller_Name", values=["KWRT_cal"])
x_targ = DF.index
fig = go.Figure()
for Chiller_name, color_thick, color_thin in zip(Chiller_List, ["#2CBDFE", "#00cc99"], ["#80d6fe", "#b3ffec"]):
    try:
        y_targ1 = DF["KWRT_cal"][Chiller_name]
        fig.add_trace(go.Scatter(name=F"{Chiller_name}-KWRT", x=x_targ, y=y_targ1, mode="lines", line=dict(color=color_thick), showlegend=True))
    except:
        print(F"{Chiller_name} Data is not sufficient to plot...\n")

fig.update_layout(
    title = dict(text=F"Chiller Performance Timeseries", x=0.04, y=0.83),
    xaxis = dict(
        showline=True, linewidth=1.2, linecolor="black",
        showticklabels=True, showgrid=True, gridcolor="rgba(230, 230, 230, 1)",
        rangeslider=dict(visible=False)),
    yaxis = dict(
        title=dict(text="Chiller Performance (KW/RT)", font=dict(size=12), standoff=0),
        showline=True, linewidth=1.2, linecolor="black",
        zeroline=True, zerolinewidth=1.2, zerolinecolor="rgba(230, 230, 230, 1)",
        showgrid=True, gridcolor="rgba(230, 230, 230, 1)"),
    legend = dict(x=1.02, y=0.5, orientation="v", bordercolor="black", borderwidth=0.5, font=dict(size=8)),
    plot_bgcolor="white", width=1000, height=400)
fig.show()

Chiller Performance Analysis

	id_point	building_id	name_point	description	units	tagged_units	equip_id	equip_type_abbr	equip_type_name	tags
7793	308715	438	AOLTW.FPMETER-053.SM-020	NaN	kWh	kwh	28550	METER	Meter	['meter', 'elecMeter']
7794	308716	438	AOLTW.FPMETER-053.WM-014	NaN	kWh	kwh	28551	METER	Meter	['meter', 'elecMeter']
7795	308008	438	Base Building AHU-37N-1_Final Filter Pressure ...	NaN	On/Off	on/off	28552	AHU	Air Handling Unit	['ahu', 'hvac']
7796	308007	438	Base Building AHU-37N-1_Cooling Coil Valve Com...	NaN	%	%	28552	AHU	Air Handling Unit	['ahu', 'hvac']
7797	308009	438	Base Building AHU-37N-1_Occupied Mode	NaN	Occ	occ	28552	AHU	Air Handling Unit	['ahu', 'hvac']
...	...	...	...	...	...	...	...	...	...	...
8899	308710	438	AOLTW.FPMETER-046.SM-019	NaN	kWh	kwh	28766	METER	Meter	['meter', 'elecMeter']
8900	308711	438	AOLTW.FPMETER-050.BM-381	NaN	kWh	kwh	28767	METER	Meter	['meter', 'elecMeter']
8901	308712	438	AOLTW.FPMETER-050.BM-382	NaN	kWh	kwh	28768	METER	Meter	['meter', 'elecMeter']
8902	308713	438	AOLTW.FPMETER-052.BM-0CT	NaN	kWh	kwh	28769	METER	Meter	['meter', 'elecMeter']
8903	308714	438	AOLTW.FPMETER-052.WM-015	NaN	kWh	kwh	28770	METER	Meter	['meter', 'elecMeter']

	308439	308007	308008	308009	308011	307611	308010	307602	307608	307605	...	308573	308574	308575	308576	308577	308718	308717	308720	308719	308721
timestamp
2020-05-01 04:00:00+00:00	NaN	99.0	0	1	1	0.00	62.0	0	70.65	70.01	...	0	1	1	0	0	46.8	1.0	44.6	1.0	1
2020-05-01 04:15:00+00:00	NaN	100.0	0	1	1	0.00	62.0	0	70.88	70.08	...	0	1	1	0	0	46.8	1.0	44.6	1.0	1
2020-05-01 04:30:00+00:00	NaN	99.0	0	1	1	0.00	62.0	0	71.05	70.13	...	0	1	1	0	0	46.8	1.0	44.6	1.0	1
2020-05-01 04:45:00+00:00	NaN	100.0	0	1	1	0.00	62.0	0	71.20	70.17	...	0	1	1	0	0	46.8	1.0	44.6	1.0	1
2020-05-01 05:00:00+00:00	NaN	100.0	0	1	1	0.00	62.0	0	71.35	70.23	...	0	1	1	0	0	46.8	1.0	44.6	1.0	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2020-10-31 23:00:00+00:00	NaN	0.0	0	0	0	60.30	62.0	0	64.00	66.66	...	0	1	1	0	0	47.3	1.0	0.0	0.0	0
2020-10-31 23:15:00+00:00	NaN	0.0	0	0	0	60.38	62.0	0	66.43	66.61	...	0	1	1	0	0	47.3	1.0	0.0	0.0	0
2020-10-31 23:30:00+00:00	NaN	0.0	0	0	0	60.31	62.0	0	64.25	66.53	...	0	1	1	0	0	47.3	1.0	0.0	0.0	0
2020-10-31 23:45:00+00:00	NaN	0.0	0	0	0	60.49	62.0	0	64.51	66.61	...	0	1	1	0	0	47.3	1.0	0.0	0.0	0
2020-11-01 00:00:00+00:00	NaN	0.0	0	0	0	60.44	62.0	0	66.30	66.55	...	0	1	1	0	0	47.3	1.0	0.0	0.0	0

	AHU_37N_1_B-AHU_CCVC	AHU_37N_1_B-AHU_OCC	AHU_37N_1_B-AHU_TAS_SP	AHU_38N_1_B-AHU_CCVC	AHU_38N_1_B-AHU_OCC	AHU_38N_1_B-AHU_TAS	AHU_38N_1_B-AHU_TAS_SP	AHU_C1_15_B-AHU_CCVC	AHU_C1_15_B-AHU_OCC	AHU_C1_15_B-AHU_TAS	...	CH_22_4_B-TCWS	CH_22_4_B-TCHS	CH_22_4_B-TCHS_SP	CH_22_4_B-IPCT	CH_22_4_B-CH_KW	CT_1_B-TCWR_1	CT_2_B-TCWR_2	CT_3_B-TCWR_3	CT_4_B-TCWR_4	CT_5_B-TCWR_5
timestamp
2020-05-01 04:00:00+00:00	99.0	1	16.666667	32.0	1	20.277778	20.255556	36.0	1	20.555556	...	17.572222	13.694444	4.444444	0.0	0.0	13.777778	12.166667	12.277778	12.444444	12.333333
2020-05-01 04:15:00+00:00	100.0	1	16.666667	33.0	1	20.277778	20.255556	36.0	1	19.722222	...	17.466667	13.505556	4.444444	0.0	0.0	13.277778	12.055556	12.166667	12.333333	12.111111
2020-05-01 04:30:00+00:00	99.0	1	16.666667	34.0	1	20.277778	20.255556	35.0	1	20.833333	...	17.444444	13.511111	4.444444	0.0	0.0	13.000000	11.888889	12.111111	12.222222	12.000000
2020-05-01 04:45:00+00:00	100.0	1	16.666667	33.0	1	20.277778	20.255556	35.0	1	19.166667	...	17.372222	13.538889	4.444444	0.0	0.0	13.000000	11.944444	12.166667	12.222222	12.055556
2020-05-01 05:00:00+00:00	100.0	1	16.666667	32.0	1	20.277778	20.255556	35.0	1	19.444444	...	17.316667	13.511111	4.444444	0.0	0.0	13.055556	11.944444	12.166667	12.222222	12.000000

	CHP_KWH	CHP_A	CHP_KW	CHP_VFD	QCH	TCHR	TCWS	TCHS	TCHS_SP	IPCT	CH_KW	TCWR_1	TCWR_2	TCWR_3	TCWR_4	TCWR_5	TCHD_cal	RT_cal	KWRT_cal	COP_cal
timestamp
2020-05-01 11:00:00+00:00	867.7	36.2	10.6	27.4	488405.802	8.622222	23.922222	3.911111	4.444444	1086.01	579.57	13.166667	15.722222	18.944444	15.888889	18.333333	4.711111	760.890873	0.761699	4.615995
2020-05-01 11:15:00+00:00	867.7	36.2	10.0	27.4	469813.125	7.955556	23.505556	3.222222	4.444444	1042.88	557.03	13.222222	15.277778	19.055556	15.722222	18.388889	4.733333	735.377687	0.757475	4.641739
2020-05-01 11:30:00+00:00	867.7	36.0	10.5	27.4	479889.552	7.461111	21.405556	2.688889	4.444444	898.14	483.28	13.277778	15.555556	18.222222	18.388889	17.111111	4.772222	757.321291	0.638144	5.509729
2020-05-01 11:45:00+00:00	867.7	36.0	10.5	27.4	479181.000	7.088889	23.005556	2.627778	4.444444	830.20	456.86	13.444444	21.722222	18.333333	18.388889	21.833333	4.461111	706.904657	0.646282	5.440347
2020-05-01 12:00:00+00:00	867.7	36.0	9.6	27.4	470664.750	6.761111	25.183333	2.672222	4.444444	765.71	431.78	13.611111	23.333333	18.722222	18.333333	23.388889	4.088889	636.407363	0.678465	5.182288

	CHP_KWH	CHP_A	CHP_KW	CHP_VFD	QCH	TCHR	TCWS	TCHS	TCHS_SP	IPCT	CH_KW	TCWR_1	TCWR_2	TCWR_3	TCWR_4	TCWR_5	TCHD_cal	RT_cal	KWRT_cal	COP_cal
timestamp
2020-07-20 15:30:00+00:00	706.0	58.1	47.7	49.2	841830.177	9.672222	30.161111	5.850000	4.444444	898.24	483.32	23.166667	23.611111	23.222222	23.222222	23.777778	3.822222	1064.041670	0.454230	7.740566
2020-07-20 15:45:00+00:00	706.0	65.1	58.2	52.8	659298.552	10.372222	29.161111	5.938889	4.444444	776.45	435.95	23.166667	23.611111	23.111111	23.222222	23.833333	4.433333	966.564235	0.451031	7.795481
2020-07-20 16:00:00+00:00	706.0	44.7	25.0	39.3	485994.000	12.072222	29.100000	6.200000	4.444444	908.74	488.47	23.277778	23.666667	23.333333	23.277778	23.888889	5.872222	943.738349	0.517590	6.793015
2020-07-20 16:15:00+00:00	706.0	49.1	32.3	39.5	521903.052	12.188889	29.761111	6.133333	4.444444	921.81	495.15	23.500000	23.777778	23.611111	23.611111	24.166667	6.055556	1045.110095	0.473778	7.421200
2020-07-20 16:30:00+00:00	706.0	46.8	28.4	39.5	500471.625	11.933333	29.844444	5.827778	4.444444	936.06	502.43	23.722222	23.722222	23.833333	23.500000	23.944444	6.105556	1010.468687	0.497225	7.071250

	TCHR		TCHS
Chiller_Name	CH_22_3	CH_22_4	CH_22_3	CH_22_4
count	12542.00	578.00	12542.00	578.00
mean	9.89	11.05	5.74	5.73
std	1.65	0.66	1.09	0.23
min	4.84	9.51	2.29	4.87
25%	8.83	10.63	5.31	5.63
50%	10.07	11.06	5.66	5.72
75%	11.32	11.54	6.59	5.83
max	15.42	12.96	9.38	7.04

	QCH
Chiller_Name	CH_22_3	CH_22_4
count	12542.00	578.00
mean	724.93	784.50
std	137.42	169.19
min	421.13	436.46
25%	620.27	778.10
50%	731.40	852.69
75%	828.49	904.04
max	1196.82	1081.70

	TCWR		TCWS
Chiller_Name	CH_22_3	CH_22_4	CH_22_3	CH_22_4
count	12542.00	578.00	12542.00	578.00
mean	21.46	21.60	27.14	26.76
std	1.60	1.26	1.83	1.78
min	9.26	19.41	15.12	23.19
25%	20.71	20.62	25.96	25.38
50%	21.58	21.46	27.16	26.59
75%	22.41	22.69	28.42	28.05
max	26.50	24.51	33.26	31.88

	RT_cal
Chiller_Name	CH_22_3	CH_22_4
count	12542.00	578.00
mean	1014.86	1362.25
std	347.59	289.81
min	114.88	826.04
25%	720.33	1085.38
50%	959.34	1398.41
75%	1284.17	1589.03
max	2335.06	2132.13

	COP_cal
Chiller_Name	CH_22_3	CH_22_4
count	12542.00	578.00
mean	6.22	6.94
std	0.45	0.45
min	2.09	5.69
25%	5.95	6.68
50%	6.28	6.97
75%	6.53	7.19
max	9.73	9.84